{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color=gray size=6 font-weight=bold face=\"微软雅黑\">Capital Bikeshare自行车数据回归分析</font> "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color=gray size=5 face=\"微软雅黑\">1.1 任务描述</font> "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "请在Capital Bikeshare （美国Washington, D.C.的一个共享单车公司）提供的自行车数据上进行回归分析。训练数据为2011年的数据，要求预测2012年每天的单车共享数量。\n",
    "\n",
    "<font color=red face=\"微软雅黑\">1) 训练数据和测试数据分割（请将2012年的数据作为测试数据）</font>；（20分）  \n",
    "2) 适当的特征工程（及数据探索）;（20分）  \n",
    "提示：  \n",
    "a) 有些特征看起来是数据值特征，其实是类别型特征，如月份、季节  \n",
    "b) 数值型特征归一化  \n",
    "c) 可以丢弃一些不必要的特征  \n",
    "3) 岭回归，并选择最佳的正则参数；（30分）  \n",
    "a) 参数调优  \n",
    "b) 结果可视化  \n",
    "4) Lasso，并选择最佳的正则参数；（30分）  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color=gray size=5 face=\"微软雅黑\">1.1.1 准备工作，导入工具包</font> "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 数据读取及基本处理\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "#查看数据分布是否对称/计算斜度\n",
    "from scipy.stats import skew\n",
    "#评价回归预测模型的性能\n",
    "from sklearn.metrics import *\n",
    "\n",
    "#可视化\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from IPython.display import display\n",
    "# Definitions\n",
    "pd.set_option('display.float_format', lambda x: '%.3f' % x)\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color=gray size=5 face=\"微软雅黑\">1.2 数据读取</font> "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "day_list : (731, 16)\n",
      "hour_list : (17379, 17)\n"
     ]
    }
   ],
   "source": [
    "# 读入训练数据和测试数据\n",
    "day_list = pd.read_csv(\"day.csv\")\n",
    "hour_list = pd.read_csv(\"hour.csv\")\n",
    "print(\"day_list : \" + str(day_list.shape))\n",
    "print(\"hour_list : \" + str(hour_list.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color=gray size=5 face=\"微软雅黑\">1.3 数据探索</font> "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color=gray size=5 face=\"微软雅黑\">1.3.1 数据规模</font> "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 731 entries, 0 to 730\n",
      "Data columns (total 16 columns):\n",
      "instant       731 non-null int64\n",
      "dteday        731 non-null object\n",
      "season        731 non-null int64\n",
      "yr            731 non-null int64\n",
      "mnth          731 non-null int64\n",
      "holiday       731 non-null int64\n",
      "weekday       731 non-null int64\n",
      "workingday    731 non-null int64\n",
      "weathersit    731 non-null int64\n",
      "temp          731 non-null float64\n",
      "atemp         731 non-null float64\n",
      "hum           731 non-null float64\n",
      "windspeed     731 non-null float64\n",
      "casual        731 non-null int64\n",
      "registered    731 non-null int64\n",
      "cnt           731 non-null int64\n",
      "dtypes: float64(4), int64(11), object(1)\n",
      "memory usage: 91.5+ KB\n"
     ]
    }
   ],
   "source": [
    "day_list.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "训练数据中空值数量为：  0\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "      <th>Null Count</th>\n",
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       "      <th>windspeed</th>\n",
       "      <td>0</td>\n",
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       "      <th>hum</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>atemp</th>\n",
       "      <td>0</td>\n",
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       "      <th>weathersit</th>\n",
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       "      <th>workingday</th>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>weekday</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>holiday</th>\n",
       "      <td>0</td>\n",
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       "      <th>mnth</th>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>yr</th>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>season</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dteday</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>instant</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Null Count\n",
       "cnt                  0\n",
       "registered           0\n",
       "casual               0\n",
       "windspeed            0\n",
       "hum                  0\n",
       "atemp                0\n",
       "temp                 0\n",
       "weathersit           0\n",
       "workingday           0\n",
       "weekday              0\n",
       "holiday              0\n",
       "mnth                 0\n",
       "yr                   0\n",
       "season               0\n",
       "dteday               0\n",
       "instant              0"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#查看训练数据哪些属性有缺失值，并统计数量\n",
    "print(\"训练数据中空值数量为： \", day_list.isnull().values.sum())\n",
    "day_list_nulls = pd.DataFrame(day_list.isnull().sum().sort_values(ascending=False)[:20], columns=['Null Count'])\n",
    "day_list_nulls"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color=red face=\"微软雅黑\">day文件样本数为731，共有属性x 15个，响应值y 1 个， 整体数据规模不算太大\n",
    "但是，数据中无缺失数据。</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 17379 entries, 0 to 17378\n",
      "Data columns (total 17 columns):\n",
      "instant       17379 non-null int64\n",
      "dteday        17379 non-null object\n",
      "season        17379 non-null int64\n",
      "yr            17379 non-null int64\n",
      "mnth          17379 non-null int64\n",
      "hr            17379 non-null int64\n",
      "holiday       17379 non-null int64\n",
      "weekday       17379 non-null int64\n",
      "workingday    17379 non-null int64\n",
      "weathersit    17379 non-null int64\n",
      "temp          17379 non-null float64\n",
      "atemp         17379 non-null float64\n",
      "hum           17379 non-null float64\n",
      "windspeed     17379 non-null float64\n",
      "casual        17379 non-null int64\n",
      "registered    17379 non-null int64\n",
      "cnt           17379 non-null int64\n",
      "dtypes: float64(4), int64(12), object(1)\n",
      "memory usage: 2.3+ MB\n"
     ]
    }
   ],
   "source": [
    "hour_list.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "测试数据中空值数量为：  0\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Null Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>cnt</th>\n",
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       "      <th>yr</th>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>mnth</th>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>hr</th>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>holiday</th>\n",
       "      <td>0</td>\n",
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       "      <th>workingday</th>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>weathersit</th>\n",
       "      <td>0</td>\n",
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       "      <th>temp</th>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>atemp</th>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>hum</th>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>windspeed</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>casual</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>instant</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Null Count\n",
       "cnt                  0\n",
       "weekday              0\n",
       "dteday               0\n",
       "season               0\n",
       "yr                   0\n",
       "mnth                 0\n",
       "hr                   0\n",
       "holiday              0\n",
       "workingday           0\n",
       "registered           0\n",
       "weathersit           0\n",
       "temp                 0\n",
       "atemp                0\n",
       "hum                  0\n",
       "windspeed            0\n",
       "casual               0\n",
       "instant              0"
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#查看训练数据哪些属性有缺失值，并统计数量\n",
    "print(\"测试数据中空值数量为： \", hour_list.isnull().values.sum())\n",
    "hour_list_nulls = pd.DataFrame(hour_list.isnull().sum().sort_values(ascending=False)[:20], columns=['Null Count'])\n",
    "hour_list_nulls"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color=red face=\"微软雅黑\">hour文件样本数为17379，共有租车属性x 16个，y属性1个，有一定的数据规模，无缺失数据。</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color=gray size=5 face=\"微软雅黑\">1.3.2 确定数据类型（查看是否需要进一步编码）</font> "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style>\n",
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       "        text-align: right;\n",
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       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
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       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>731.000</td>\n",
       "      <td>731.000</td>\n",
       "      <td>731.000</td>\n",
       "      <td>731.000</td>\n",
       "      <td>731.000</td>\n",
       "      <td>731.000</td>\n",
       "      <td>731.000</td>\n",
       "      <td>731.000</td>\n",
       "      <td>731.000</td>\n",
       "      <td>731.000</td>\n",
       "      <td>731.000</td>\n",
       "      <td>731.000</td>\n",
       "      <td>731.000</td>\n",
       "      <td>731.000</td>\n",
       "      <td>731.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>366.000</td>\n",
       "      <td>2.497</td>\n",
       "      <td>0.501</td>\n",
       "      <td>6.520</td>\n",
       "      <td>0.029</td>\n",
       "      <td>2.997</td>\n",
       "      <td>0.684</td>\n",
       "      <td>1.395</td>\n",
       "      <td>0.495</td>\n",
       "      <td>0.474</td>\n",
       "      <td>0.628</td>\n",
       "      <td>0.190</td>\n",
       "      <td>848.176</td>\n",
       "      <td>3656.172</td>\n",
       "      <td>4504.349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>211.166</td>\n",
       "      <td>1.111</td>\n",
       "      <td>0.500</td>\n",
       "      <td>3.452</td>\n",
       "      <td>0.167</td>\n",
       "      <td>2.005</td>\n",
       "      <td>0.465</td>\n",
       "      <td>0.545</td>\n",
       "      <td>0.183</td>\n",
       "      <td>0.163</td>\n",
       "      <td>0.142</td>\n",
       "      <td>0.077</td>\n",
       "      <td>686.622</td>\n",
       "      <td>1560.256</td>\n",
       "      <td>1937.211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.059</td>\n",
       "      <td>0.079</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.022</td>\n",
       "      <td>2.000</td>\n",
       "      <td>20.000</td>\n",
       "      <td>22.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>183.500</td>\n",
       "      <td>2.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>4.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.337</td>\n",
       "      <td>0.338</td>\n",
       "      <td>0.520</td>\n",
       "      <td>0.135</td>\n",
       "      <td>315.500</td>\n",
       "      <td>2497.000</td>\n",
       "      <td>3152.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>366.000</td>\n",
       "      <td>3.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>7.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>3.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.498</td>\n",
       "      <td>0.487</td>\n",
       "      <td>0.627</td>\n",
       "      <td>0.181</td>\n",
       "      <td>713.000</td>\n",
       "      <td>3662.000</td>\n",
       "      <td>4548.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>548.500</td>\n",
       "      <td>3.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>10.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>5.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>2.000</td>\n",
       "      <td>0.655</td>\n",
       "      <td>0.609</td>\n",
       "      <td>0.730</td>\n",
       "      <td>0.233</td>\n",
       "      <td>1096.000</td>\n",
       "      <td>4776.500</td>\n",
       "      <td>5956.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>731.000</td>\n",
       "      <td>4.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>12.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>6.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>3.000</td>\n",
       "      <td>0.862</td>\n",
       "      <td>0.841</td>\n",
       "      <td>0.973</td>\n",
       "      <td>0.507</td>\n",
       "      <td>3410.000</td>\n",
       "      <td>6946.000</td>\n",
       "      <td>8714.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       instant  season      yr    mnth  holiday  weekday  workingday  \\\n",
       "count  731.000 731.000 731.000 731.000  731.000  731.000     731.000   \n",
       "mean   366.000   2.497   0.501   6.520    0.029    2.997       0.684   \n",
       "std    211.166   1.111   0.500   3.452    0.167    2.005       0.465   \n",
       "min      1.000   1.000   0.000   1.000    0.000    0.000       0.000   \n",
       "25%    183.500   2.000   0.000   4.000    0.000    1.000       0.000   \n",
       "50%    366.000   3.000   1.000   7.000    0.000    3.000       1.000   \n",
       "75%    548.500   3.000   1.000  10.000    0.000    5.000       1.000   \n",
       "max    731.000   4.000   1.000  12.000    1.000    6.000       1.000   \n",
       "\n",
       "       weathersit    temp   atemp     hum  windspeed   casual  registered  \\\n",
       "count     731.000 731.000 731.000 731.000    731.000  731.000     731.000   \n",
       "mean        1.395   0.495   0.474   0.628      0.190  848.176    3656.172   \n",
       "std         0.545   0.183   0.163   0.142      0.077  686.622    1560.256   \n",
       "min         1.000   0.059   0.079   0.000      0.022    2.000      20.000   \n",
       "25%         1.000   0.337   0.338   0.520      0.135  315.500    2497.000   \n",
       "50%         1.000   0.498   0.487   0.627      0.181  713.000    3662.000   \n",
       "75%         2.000   0.655   0.609   0.730      0.233 1096.000    4776.500   \n",
       "max         3.000   0.862   0.841   0.973      0.507 3410.000    6946.000   \n",
       "\n",
       "           cnt  \n",
       "count  731.000  \n",
       "mean  4504.349  \n",
       "std   1937.211  \n",
       "min     22.000  \n",
       "25%   3152.000  \n",
       "50%   4548.000  \n",
       "75%   5956.000  \n",
       "max   8714.000  "
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#对数据值型特征，用常用统计量观察其分布\n",
    "day_list.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>hr</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>17379.000</td>\n",
       "      <td>17379.000</td>\n",
       "      <td>17379.000</td>\n",
       "      <td>17379.000</td>\n",
       "      <td>17379.000</td>\n",
       "      <td>17379.000</td>\n",
       "      <td>17379.000</td>\n",
       "      <td>17379.000</td>\n",
       "      <td>17379.000</td>\n",
       "      <td>17379.000</td>\n",
       "      <td>17379.000</td>\n",
       "      <td>17379.000</td>\n",
       "      <td>17379.000</td>\n",
       "      <td>17379.000</td>\n",
       "      <td>17379.000</td>\n",
       "      <td>17379.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>8690.000</td>\n",
       "      <td>2.502</td>\n",
       "      <td>0.503</td>\n",
       "      <td>6.538</td>\n",
       "      <td>11.547</td>\n",
       "      <td>0.029</td>\n",
       "      <td>3.004</td>\n",
       "      <td>0.683</td>\n",
       "      <td>1.425</td>\n",
       "      <td>0.497</td>\n",
       "      <td>0.476</td>\n",
       "      <td>0.627</td>\n",
       "      <td>0.190</td>\n",
       "      <td>35.676</td>\n",
       "      <td>153.787</td>\n",
       "      <td>189.463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>5017.029</td>\n",
       "      <td>1.107</td>\n",
       "      <td>0.500</td>\n",
       "      <td>3.439</td>\n",
       "      <td>6.914</td>\n",
       "      <td>0.167</td>\n",
       "      <td>2.006</td>\n",
       "      <td>0.465</td>\n",
       "      <td>0.639</td>\n",
       "      <td>0.193</td>\n",
       "      <td>0.172</td>\n",
       "      <td>0.193</td>\n",
       "      <td>0.122</td>\n",
       "      <td>49.305</td>\n",
       "      <td>151.357</td>\n",
       "      <td>181.388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.020</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4345.500</td>\n",
       "      <td>2.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>4.000</td>\n",
       "      <td>6.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.340</td>\n",
       "      <td>0.333</td>\n",
       "      <td>0.480</td>\n",
       "      <td>0.104</td>\n",
       "      <td>4.000</td>\n",
       "      <td>34.000</td>\n",
       "      <td>40.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>8690.000</td>\n",
       "      <td>3.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>7.000</td>\n",
       "      <td>12.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>3.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.500</td>\n",
       "      <td>0.485</td>\n",
       "      <td>0.630</td>\n",
       "      <td>0.194</td>\n",
       "      <td>17.000</td>\n",
       "      <td>115.000</td>\n",
       "      <td>142.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>13034.500</td>\n",
       "      <td>3.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>10.000</td>\n",
       "      <td>18.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>5.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>2.000</td>\n",
       "      <td>0.660</td>\n",
       "      <td>0.621</td>\n",
       "      <td>0.780</td>\n",
       "      <td>0.254</td>\n",
       "      <td>48.000</td>\n",
       "      <td>220.000</td>\n",
       "      <td>281.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>17379.000</td>\n",
       "      <td>4.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>12.000</td>\n",
       "      <td>23.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>6.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>4.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.851</td>\n",
       "      <td>367.000</td>\n",
       "      <td>886.000</td>\n",
       "      <td>977.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        instant    season        yr      mnth        hr   holiday   weekday  \\\n",
       "count 17379.000 17379.000 17379.000 17379.000 17379.000 17379.000 17379.000   \n",
       "mean   8690.000     2.502     0.503     6.538    11.547     0.029     3.004   \n",
       "std    5017.029     1.107     0.500     3.439     6.914     0.167     2.006   \n",
       "min       1.000     1.000     0.000     1.000     0.000     0.000     0.000   \n",
       "25%    4345.500     2.000     0.000     4.000     6.000     0.000     1.000   \n",
       "50%    8690.000     3.000     1.000     7.000    12.000     0.000     3.000   \n",
       "75%   13034.500     3.000     1.000    10.000    18.000     0.000     5.000   \n",
       "max   17379.000     4.000     1.000    12.000    23.000     1.000     6.000   \n",
       "\n",
       "       workingday  weathersit      temp     atemp       hum  windspeed  \\\n",
       "count   17379.000   17379.000 17379.000 17379.000 17379.000  17379.000   \n",
       "mean        0.683       1.425     0.497     0.476     0.627      0.190   \n",
       "std         0.465       0.639     0.193     0.172     0.193      0.122   \n",
       "min         0.000       1.000     0.020     0.000     0.000      0.000   \n",
       "25%         0.000       1.000     0.340     0.333     0.480      0.104   \n",
       "50%         1.000       1.000     0.500     0.485     0.630      0.194   \n",
       "75%         1.000       2.000     0.660     0.621     0.780      0.254   \n",
       "max         1.000       4.000     1.000     1.000     1.000      0.851   \n",
       "\n",
       "         casual  registered       cnt  \n",
       "count 17379.000   17379.000 17379.000  \n",
       "mean     35.676     153.787   189.463  \n",
       "std      49.305     151.357   181.388  \n",
       "min       0.000       0.000     1.000  \n",
       "25%       4.000      34.000    40.000  \n",
       "50%      17.000     115.000   142.000  \n",
       "75%      48.000     220.000   281.000  \n",
       "max     367.000     886.000   977.000  "
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#对数据值型特征，用常用统计量观察其分布\n",
    "hour_list.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color=red face=\"微软雅黑\">通过查看cnt可以看到（day数据集）每小时使用单车人数集中分布在3152(四分之一分位数)-5956(四分之三分位数)这个区间。</font>  \n",
    "  \n",
    "  \n",
    "<font color=red face=\"微软雅黑\">通过查看cnt可以看到（hour数据集）每小时使用单车人数集中分布在40(四分之一分位数)-281(四分之三分位数)这个区间</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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3KCIRmzRiKOflprNs7T7dmd7DNIgubTpe18ia4grOGZt+ypVX0T5oKtKWJeeN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\n/wMpkOtklNbN1QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x218706edcc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The skewness is : -0.047352780119\n"
     ]
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(day_list.cnt, bins=40, kde=True)\n",
    "plt.show()\n",
    "print ('The skewness is :', day_list.cnt.skew())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### day数据的cnt的偏度为-0.04，趋近于0符合正态分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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MBDMLAfcCVwHlwPVmVt6t2k1Ak7vPAu4G7gq2LQeWAGcAi4D7gs9rAy5193nAfGCRmZ0/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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x218706ed860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The skewness is : 1.27741160375\n"
     ]
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(hour_list.cnt, bins=40, kde=True)\n",
    "plt.show()\n",
    "print ('The skewness is :', hour_list.cnt.skew())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### hour数据的cnt偏度为1.1316，数据出现了偏斜（分布右侧有个较长的尾部，所以分布属于正向偏差），则可以通过取对数来对目标变量进行对数转换以此来改善数据的线性度，让偏斜度更加趋近于0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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p7Bvkh+9V8LuKZq6Zn8p9HyiwwA9RawpSANhxIrRa+75cyM0Cqr2e1wBrfHz9\n8c7NGnuQiNwP3A+QmxuaK9SHotn+C6CiqYtndlTTNzjMp4tzuDwnaUbexwSGS+YmEB8VzrbKFtYt\nf18sBS1fQn+8q1q+rkDg07mq+iTwJEBxcXHorm5gZsTg8Ai/PXiaLcebSYmN5PNX5zM3MXT+nDfj\nc4UJxfnJbKtodrqUWeVL6NcAOV7Ps4E6H1+/BvjQmHPf8vFcYy7aG4fr+c/Xj9HcPcCVhSmsXTqX\nyHAbtGZGrS5I5c0jjTR19ZMWF+V0ObPCl9DfARSJSAFQC9wN3OPj678C/LOIJHue3wx8fdJVGjOB\nsV1FtW29vFp2mqP1XaTFRfLH1xSwICPOoeqMv1pT6OnXr2zh1mVzHa5mdkwY+qo6JCIPMhrgLmC9\nqh4UkUeAUlUtEZErgBeAZOCjIvJNVV2qqi0i8k+MfnAAPKKqoXXVxMyaEVUqGrt5r7yRo/VdREeE\n8ZFLM7lyfirhYda6N+936bxEYiJcbLPQP5eqbgI2jdn2sNfjHYx23Yx37npg/UXUaMx5qSplpzp4\n7VA9u6taae0ZJDbSxc1L5nBlYaqtU2suKDI8jJV5SWyrDJ22qE3DYAJK3+AwFY3d7Kluo/RkC1uP\nN1PX3gdAYXosNy3JZOm8BCJsVkzjozUFqXz3taO09wyS6A7+4bsW+uZ9LnRX6mzdUNU/NExlUzdH\n67s4Vt/J0fpOjtV3caK5mxHP+K6U2EiuyE/mKzcW0dYzaOPtzZSsKUhBFbZVNnPz0kyny5lxFvrG\nUUMjIzR1DdDQ0Ud9Rz8NnX388L0KTjb3MOxJd1eYkJfqpmhOHLddNpeiOfFcOi+BgrRYREZHBdv0\nCWaqlucmER0RxpbjFvrGTIuBoRE6+gZp7x2ko3eQ5u4B6jv6aOjsp7mr/2zLXYDUuEhW5SVz27K5\nLMiIY+GceArTY4kKt755MzOiwl2sLkhlc3mT06XMCgt9c9G6+4c41tBFeUMXp9p6OdXRx+n2Pk61\n93G6vZfWnsFzjhdGu2bmJESzdF4CGfHRzEmIIi0uighX2Kx1IRlzxjXzU/mXlw/T0NFHRkK00+XM\nKAt9MykbtlWdHRp5tL6TYw2d1Hf0n3NMSmwkmQnRZCVFsyoviYaOfhJjIkiIiSDR82UXWo0/uWbB\n6Fq5m4838bEV4w5EDBoW+iFssv3gvQPDbD/RwvbKZlp7Bgn39LVfekkin1yVQ9GcOLKSYt43THKy\n7+MPF5IXKg1zAAAOrklEQVRNaFkyN4EkdwSby5st9I0ZHlG2VTbz+qEGegeHKUiL5ealmVySmXB2\nSoO1lwb/BTATXMY2LrKTYni1rB5VPTtAIBhZ6AeJmWod17X18mxpNQ2d/cxPj+XWS+eG1NzjJnTM\nz4jjQF0HlU3dFKYH75QdFvpmXCOqvHesiVfL6nFHufjclXkszowP6haQCW3zPUG/+Xizhb6ZXU73\naQ8Nj/Dcrhr21bSzdF4CH1uehTvKflVMcEuNjSQpJoJ3jzbyuSvznC5nxtj/yeYcvQPD/HzbSSqb\nurllaSbXFaVZ696EBBFhYWY875U30T80HLT3hljom7Paegb4yZYTNHcN8KniHJZPYmUpuyPWBIPF\nc+LZXtnC9soWri1Kd7qcGWGDpQ0wesH28beP09E3yOevyZ9U4BsTLArT44gKD+ONww1OlzJjrKVv\nONbQyYZtVURHuLj/uvlk+vEdifYXhZlJkeFhXDU/lTcPN/APH13qdDkzwlr6IW5XVSs/3XKCZHck\nX/qgfwe+MbPhhsUZnGjuobKp2+lSZoSFfohSVd443MBzO2soTIvj/usKSYyxqYmNuX5RBkDQdvH4\nFPoislZEjohIuYg8NM7+KBF5xrN/m4jke7bni0iviOzxfD0+veWbqRgaHuFvX9jPa4fqWZGTxB9e\nnWcrTBnjkZPipigjjjeDNPQn7NMXERfwGHATUAPsEJESVS3zOuw+oFVVF4jI3cC/AZ/27Duuqsun\nuW4zRR19g/zZ07t560gj1y9K58ZL5tiQTGPGuGFxBus3V9LRN0hCkC3O48uF3NVAuapWAIjIRmAd\n4B3664B/9Dx+DvieBGmSnO9CYiBMBFbR2MUXflZKVXMP//LxZag6XZEx/umWSzN54p0KXiur5+Mr\ng2sCNl+6d7KAaq/nNZ5t4x6jqkNAO5Dq2VcgIrtF5G0RufYi6zVT9NaRBtY9tpm2nkGe+sIaPrPa\n/z+kjHHKipwkspJi+PW+U06XMu18Cf3xWuxj24jnO+YUkKuqK4CvAhtEJOF9byByv4iUikhpY2Oj\nDyUZX6kqP3ingj/+yQ6yk92UPHgNawpTJz7RmBAmInxkWSbvHGukvXdw4hMCiC+hXwPkeD3PBurO\nd4yIhAOJQIuq9qtqM4Cq7gSOAwvHvoGqPqmqxapanJ4enHfBOaFvcJhnSqt5dNMh1l6ayfN/ehXZ\nyW6nyzImINx22TwGh5VXy+qdLmVa+RL6O4AiESkQkUjgbqBkzDElwL2ex3cBb6iqiki650IwIlII\nFAEV01O6uZDqlh7+641jHKht569vWcRj96zEHWn34hnjq8uzEz1dPGPbuIFtwhRQ1SEReRB4BXAB\n61X1oIg8ApSqagnwI+B/RKQcaGH0gwHgOuARERkChoEvqWrLTHwjZtSIKu8ea+LVstMkREfwxWsL\neeD6BU6XZUzAERFuv2wuP3qvkvaeQRLdwTGKx6emn6puAjaN2faw1+M+4JPjnPc88PxF1mh81NE7\nyHO7aihv6GLpvAQ+viKbmEgbf2/MVN122VyeeKeClw+c4u4gGfxgf+8HAVVlx4kWXj5wiqFh5c7l\nWVyRn2zj7425SMuyElmQEcfGHdVBE/o2DUOA21fTxqef3MoLu2uZmxjDVz5cxOqCFAt8Y6aBiPCZ\n1bnsqW6jrK7D6XKmhbX0A1RZXQePv32ckr11pMZG8rHlWazKTybMwt6YizL2BkxVJTxM+OavDvLM\nn1zlUFXTx0I/gPQNDvPi7lqe3l7FtsoWYiJcPHj9Av7kg4X8am/w3URijD9wR4ZzaVYie6rb6BkY\nCvhRcIFdfQjoHxzm8OlO9te2c7S+k6ERJSsphr/9yGI+XZwbNCMKjPFnq/NT2FPdxkt7T/GpK3Im\nPsGPWej7qZrWHrZWNLOvpp2hESUhOpzVBSn85c0LWZGTTFiYdeMYM1vyUt1kxEfxs60n+GRxdkBf\nM7PQ9zMH69r5wbsVVDZ1E+kKY2VeMsuzk8hNdRMmwqq8FKdLNCbkiAgfWJDGL3fX8uaRBm5YPMfp\nkqbMQt9PdPYN8i8vH+bp7VXERLi4bdlcVuUl2zz3xviJFbnJ7DjZwr+/dozrF2UEbGvfQt8P7Ktp\n48tP76a6pYc/urqArKQYu6nKGD/jChO+fH0RX3t+X0C39m2cvoNUlZ9sruQT39/C4NAIz/zJVTz8\n0SUW+Mb4qY+tzCInJYZ/f+0YGqALUljoO6R3YJivPruXf/xVGR9cmM6mr1zLFfnWX2+MP4twhfHl\n64vYV9POi3tqnS5nSqx7ZwLd/UNsLm9if207h051Ut/RR2yUi4z4aC6ZmzClxcTLGzr58tN7OHy6\ng6/etJAHr19go3GMCRCfWJXNxh1VfPNXZVyzII2M+GinS5oUC/3zaOjs4yebT/DzrSfp6BvCFSYU\npsXS2jNAV/8QfYMjlOytIzs5hivyU/jYiqwJu2VUlZ9vq+LRX5fhjgxn/b1XcP3ijFn6jowx08EV\nJnzrrsv5yH++yzdePMDjn10VUBd1LfTHGBoe4SdbTvCdV4/SOzjM2qWZfO6qPFbkJBMT6Tp7i3ZD\nZx9ldR3srWnjhd21vH6onnXLs1i3fB6r8s6d7Gx4RHn5wCm+90Y5h093ct3CdP7vXZeRkRBYLQRj\nzKgFGXH8xY0L+bffHOZ/99Rx54qxK8j6Lwt9Lwdq23nol/s4UNvBhxal8/DtSyhMjxv32Iz4aDIW\nRfPBhemcaO7hdEcfz5ZW8z9bT5IWF0lBWiyZiTGcauvlSH0nnX1DzE+P5Tufupw7l2dZd44xAe6L\n1xbw2qF6vvb8PuYkRHPV/MBYhtRCn9F+++++epT1mytJiY3ie/es4LZlc336k01EKEiL5e9uu4Su\n/iFeOXCarRXNnGzpYXdVK/MSY1i3fB7XzE/j5qWZuCzsjQkK4a4wfviHxXzqid/xxZ+V8vQXr2RZ\ndqLTZU0opENfVSnZW8e/bDrM6Y4+7lmTy9+sXTyli7MAcVHhfGJVNp9YlT3NlRpj/FFybCT/c98a\nPvH9LXxu/Ta+fdfl3LTEv8fv+zRkU0TWisgRESkXkYfG2R8lIs949m8TkXyvfV/3bD8iIrdMX+lT\nNzKivFZWz8e/v4WvbNxDenwUz//pVfzzx5ZNOfCNMaEpMzGap794JfMSY/jiz0r5xosH6Oofcrqs\n85qwpe9Z2Pwx4CagBtghIiWqWuZ12H1Aq6ouEJG7gX8DPi0iSxhdL3cpMA94TUQWqurwdH8jvjje\n2MVvD9bzi53VVDR2My8xmm994jLuWpVtfezGmCnLTXXzwgNX8+3fHOGH71Xy4p5a7lmTy2fX5JGT\n4na6vHP40r2zGihX1QoAEdkIrAO8Q38d8I+ex88B35PRDvF1wEZV7QcqPQunrwZ+Nz3l/56q0j0w\nTFffEF39g7R0D3KqvZea1l7K6jrYV9tGdUsvAJfnJPEfdy/nI8vmEuGy+9OMMRcvKtzF39++hNsv\nn8cP3qngB+9U8MTbFeSmuLmyMIWijHjyUt2kxUcRGxmOO9JFXFQ47igXLs/1wzCRGW+A+hL6WUC1\n1/MaYM35jlHVIRFpB1I927eOOXdGxjY1dvWz+tHXx92XkxLDpfMSue+aAm5amklWUsxMlGCMMSzP\nSeKxP1hJdUsPr5bVs+V4M78tq+fZ0hqfzn3xgWtmtD5fQn+8j52xk06c7xhfzkVE7gfu9zztEpEj\nPtTls5PAe8Dj0/NyaUDT2I1/MD2vPaGpvM9s1TbGuD8nMy77WfnG8Z/TTP+/dBKQB6d8ep4vB/kS\n+jWA91Ix2UDdeY6pEZFwIBFo8fFcVPVJ4ElfCnaaiJSqarHTdfg7+zn5zn5WvrGf0/TwpUN7B1Ak\nIgUiEsnohdmSMceUAPd6Ht8FvKGjU9CVAHd7RvcUAEXA9ukp3RhjzGRN2NL39NE/CLwCuID1qnpQ\nRB4BSlW1BPgR8D+eC7UtjH4w4DnuWUYv+g4BDzg1cscYYwxIoM4J7RQRud/THWUuwH5OvrOflW/s\n5zQ9LPSNMSaE2CB1Y4wJIRb6PppoKgozSkRyRORNETkkIgdF5CtO1+TPRMQlIrtF5CWna/FXIpIk\nIs+JyGHP79VVTtcUyKx7xweeqSiO4jUVBfCZMVNRGEBE5gJzVXWXiMQDO4E77Wc1PhH5KlAMJKjq\n7U7X449E5KfAu6r6Q88IQreqtjldV6Cylr5vzk5FoaoDwJmpKMwYqnpKVXd5HncCh5ihu7ADnYhk\nA7cBP3S6Fn8lIgnAdYyOEERVByzwL46Fvm/Gm4rCgmwCntlWVwDbnK3Eb/078DVgxOlC/Fgh0Aj8\n2NMN9kMRiXW6qEBmoe8bn6aTML8nInHA88Cfq2qH0/X4GxG5HWhQ1Z1O1+LnwoGVwPdVdQXQDdg1\ntYtgoe8bn6aTMKNEJILRwH9KVX/pdD1+6hrgDhE5wWh34Q0i8nNnS/JLNUCNqp75a/E5Rj8EzBRZ\n6PvGl6koDOCZUvtHwCFV/Y7T9fgrVf26qmaraj6jv09vqOpnHS7L76jqaaBaRBZ5Nn2Yc6d1N5MU\n0ssl+up8U1E4XJa/ugb4HLBfRPZ4tv2tqm5ysCYT2L4MPOVpcFUAf+RwPQHNhmwaY0wIse4dY4wJ\nIRb6xhgTQiz0jTEmhFjoG2NMCLHQN8aYEGKhb8wUiUi+iNzjdB3GTIaFvjFTlw9Y6JuAYuP0jRlD\nRP4Q+CtG51faBwwDHYxOgZwJfE1VnxORrcAlQCXwU1X9rkMlG+MzC31jvIjIUuCXwDWq2iQiKcB3\ngFjg08BioERVF4jIh4C/snnwTSCx7h1jznUD8JyqNgGoaotn+4uqOuJZDGaOY9UZc5Es9I05lzD+\ntNn9Y44xJiBZ6BtzrteBT4lIKoCne+d8OoH4WanKmGlis2wa40VVD4rIo8DbIjIM7L7A4fuAIRHZ\nC/zELuSaQGAXco0xJoRY944xxoQQC31jjAkhFvrGGBNCLPSNMSaEWOgbY0wIsdA3xpgQYqFvjDEh\nxELfGGNCyP8Hl1rCV+ZY7TIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x218718918d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The skewness is : -0.936181764548\n"
     ]
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "train_log = np.log(hour_list.cnt)\n",
    "sns.distplot(train_log, bins=40, kde=True)\n",
    "plt.show()\n",
    "print ('The skewness is :', train_log.skew())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 现在偏斜度还是很大，不符合正太分布"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color=red face=\"微软雅黑\" size=3>在hour和day的数据中， 都有instant属性，而这个属性只是record index和cnt关系不大，可以删除。</font> "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "hour_list = hour_list.drop(['instant'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "day_list = day_list.drop(['instant'],axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 然后来看下两两特征之间的相关系数（只计算数值类型，需要把数值类型和object类型区分开）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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AEc65jf7rR4Ghu3nfQGAgwCeN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\n0msjgaOVTsUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x21872e2aeb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "day_numerical_type = day_list.select_dtypes(include=[np.number])\n",
    "day_object_type = day_list.select_dtypes(exclude=[np.number])\n",
    "# encoding=utf-8\n",
    "day_corr = day_numerical_type.corr().abs()  # 通常认为相关系数的绝对值大于0.5的为强相关\n",
    "plt.subplots(figsize=(30, 20))\n",
    "sns.heatmap(day_corr, annot=True)\n",
    "sns.heatmap(day_corr, mask=day_corr<1, cbar=False)\n",
    "plt.savefig('bikes_corr_day.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cnt          1.000\n",
      "registered   0.946\n",
      "casual       0.673\n",
      "atemp        0.631\n",
      "temp         0.627\n",
      "yr           0.567\n",
      "season       0.406\n",
      "weathersit   0.297\n",
      "mnth         0.280\n",
      "windspeed    0.235\n",
      "Name: cnt, dtype: float64 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "print (day_corr['cnt'].sort_values(ascending=False)[:10], '\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color=red >从图中可以看到与cnt最相关度比较高的属性。其中registered和casual的相关度分别达到0.95及0.67, temp和atemp相关度分别达到0.63(一般超过0.5就算强相关)，呈高度线性相关态势，所以registered和casual可以只取一个，temp和atempBit也一样。</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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RAAAg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szpSGxxNv5etJVHmjj26z83ncO5e2ZJLBwQtv8UFyMSzicijpzBLYs7Vo8HZm7cHVWuVoFT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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2187132e978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hour_numerical_type = hour_list.select_dtypes(include=[np.number])\n",
    "hour_object_type = hour_list.select_dtypes(exclude=[np.number])\n",
    "# encoding=utf-8\n",
    "hour_corr = hour_numerical_type.corr().abs()  # 通常认为相关系数的绝对值大于0.5的为强相关\n",
    "plt.subplots(figsize=(30, 20))\n",
    "sns.heatmap(hour_corr, annot=True)\n",
    "sns.heatmap(hour_corr, mask=hour_corr<1, cbar=False)\n",
    "plt.savefig('bikes_corr_hour.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cnt          1.000\n",
      "registered   0.972\n",
      "casual       0.695\n",
      "temp         0.405\n",
      "atemp        0.401\n",
      "hr           0.394\n",
      "hum          0.323\n",
      "yr           0.250\n",
      "season       0.178\n",
      "weathersit   0.142\n",
      "Name: cnt, dtype: float64 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "print (hour_corr['cnt'].sort_values(ascending=False)[:10], '\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color=red >从图中可以看到与cnt最相关度比较高的属性。其中registered和casual的相关度分别达到0.969及0.715(一般超过0.5就算强相关)，呈高度线性相关态势，所以这两列特征可以只取一个，temp和atempBit也一样。</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color=gray face=\"微软雅黑\" size=4>特征值处理</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "由于dteday属性是每天的日期是字符串，而且其它属性已经有年份月份和哪天，所以dteday也可以删除。  \n",
    "四个与cnt相关度较高的属性，分别删除temp和casual，个人认为atemp体感更能体现用户愿不愿意骑车，还有cnt = casual + registered，都是输出，只要关注cnt即可，casual和registered都可以删除"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "day_list = day_list.drop(['dteday'],axis=1)\n",
    "hour_list = hour_list.drop(['dteday'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "day_list = day_list.drop(['temp'],axis=1)\n",
    "hour_list = hour_list.drop(['temp'],axis=1)\n",
    "day_list = day_list.drop(['casual'],axis=1)\n",
    "hour_list = hour_list.drop(['casual'],axis=1)\n",
    "day_list = day_list.drop(['registered'],axis=1)\n",
    "hour_list = hour_list.drop(['registered'],axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.3.3 离群点检测"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 查看下与cnt相关度高的几个属性的类型、取值以及散点图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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1Ud0ntBVKsc4g7t0bfy++ZgvEtzFTPPW48nOvGwEVKbmhxSbrDIfvWNeIqU9u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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2186e148080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(day_list.atemp, day_list.cnt, c = \"blue\")\n",
    "plt.title(\"Looking for outliers\")\n",
    "plt.xlabel(\"atemp\")\n",
    "plt.ylabel(\"cnt\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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hr702vsF3ceSI/tj5ebdA14ltqOfn14rpzExRVxe279U6cpB7hUNELhKR9wM4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\nEEIIiYLCQQghJIr/D0kCkr9mtcBzAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x218716cf7b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(day_list.hum, day_list.cnt, c = \"red\")\n",
    "plt.title(\"Looking for outliers\")\n",
    "plt.xlabel(\"hum\")\n",
    "plt.ylabel(\"cnt\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#剔除离群点大于2.5\n",
    "day_list = day_list[day_list.hum > 0.1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Kbd7cfTlNC1F4ED3GpePLeDPdU5cg6xWXmEEUbXGpAQTJRTiEfnHVunW/ui1f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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x21871ed5710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(day_list.hum, day_list.cnt, c = \"red\")\n",
    "plt.title(\"Looking for outliers\")\n",
    "plt.xlabel(\"hum\")\n",
    "plt.ylabel(\"cnt\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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PopVtnOw5sscyaQFgKbvvpiCOTRwL1DDMrhSkhLo39cjj2AdgHzM/rb3/GiqK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\nhCgOQRAEIRCiOARBEIRA/H+qvGgE9Z7dcQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x21872ad78d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(hour_list.atemp, hour_list.cnt, c = \"green\")\n",
    "plt.title(\"Looking for outliers\")\n",
    "plt.xlabel(\"atemp\")\n",
    "plt.ylabel(\"cnt\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 上图中，可以看出不管是day数据还是hour数据atemp属性点相对集中，不用剔除离群点"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.3.3 数据分离"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "训练数据和测试数据分割（请将2012年的数据作为测试数据）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "day_test = day_list[day_list.yr == 1]\n",
    "day_train = day_list[day_list.yr == 0]        \n",
    "hour_test = hour_list[hour_list.yr == 1]\n",
    "hour_train = hour_list[hour_list.yr == 0]     "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2 搭建模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#从原始数据中分离输入特征X和输出y\n",
    "y_d_train = day_train['cnt'].values\n",
    "X_d_train = day_train.drop('cnt', axis = 1)\n",
    "\n",
    "y_h_train = hour_train['cnt'].values\n",
    "X_h_train = hour_train.drop('cnt', axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1 从训练集中分离一部分作为测试集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\envs\\python3\\lib\\site-packages\\sklearn\\cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "#将数据分割训练数据和测试数据\n",
    "from sklearn.cross_validation import train_test_split\n",
    "\n",
    "#随机采样25%的数据构建测试样本，其余作为训练样本\n",
    "X_day_train, X_day_test, y_day_train, y_day_test = train_test_split(X_d_train, y_d_train, random_state = 33, test_size = 0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\envs\\python3\\lib\\site-packages\\sklearn\\utils\\validation.py:444: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "#分别初始化对特征和目标值的标准化器\n",
    "ss_X = StandardScaler()\n",
    "ss_y = StandardScaler()\n",
    "\n",
    "#分别对训练和测试数据数据的特征以及目标值进行标准化处理\n",
    "X_day_train = ss_X.fit_transform(X_day_train)\n",
    "X_day_test = ss_X.transform(X_day_test)\n",
    "\n",
    "y_day_train = ss_y.fit_transform(y_day_train.reshape(-1, 1))\n",
    "y_day_test = ss_y.transform(y_day_test.reshape(-1, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#将数据分割训练数据和测试数据\n",
    "from sklearn.cross_validation import train_test_split\n",
    "\n",
    "#随机采样25%的数据构建测试样本，其余作为训练样本\n",
    "X_hour_train, X_hour_test, y_hour_train, y_hour_test = train_test_split(X_h_train, y_h_train, random_state = 33, test_size = 0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\envs\\python3\\lib\\site-packages\\sklearn\\utils\\validation.py:444: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "#分别初始化对特征和目标值的标准化器\n",
    "ss_X = StandardScaler()\n",
    "ss_y = StandardScaler()\n",
    "\n",
    "#分别对训练和测试数据数据的特征以及目标值进行标准化处理\n",
    "X_hour_train = ss_X.fit_transform(X_hour_train)\n",
    "X_hour_test = ss_X.transform(X_hour_test)\n",
    "\n",
    "y_hour_train = ss_y.fit_transform(y_hour_train.reshape(-1, 1))\n",
    "y_hour_test = ss_y.transform(y_hour_test.reshape(-1, 1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2 模型测试（三种）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2.1 OLS缺省参数的线性回归模型"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "对day数据进行OLS缺省参数的线性回归"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  3.21352709e-01,  -5.55111512e-17,  -4.42061312e-02,\n",
       "         -5.77591170e-02,   4.07359191e-02,   9.70216531e-03,\n",
       "         -2.12775467e-01,   6.45681674e-01,  -3.62316163e-02,\n",
       "         -1.11442815e-01]])"
      ]
     },
     "execution_count": 293,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#线性回归\n",
    "#class sklearn,linear_model.LinearRegression(fit_intercept=True, normalize=False, copy_X=True, n_jobs=1)\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "#使用没人配置初始化\n",
    "lr = LinearRegression()\n",
    "\n",
    "#训练模型参数\n",
    "model = lr.fit(X_day_train, y_day_train)\n",
    "\n",
    "#预测，下面计算score会自动调用predict\n",
    "lr_y_predict = model.predict(X_day_test)\n",
    "lr_y_predict_train = model.predict(X_day_train)\n",
    "\n",
    "#显示特征的回归系数\n",
    "lr.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The r2 score of LinearRegression on test is 0.754401175592\n",
      "The r2 score of LinearRegression on train is 0.752100376118\n"
     ]
    }
   ],
   "source": [
    "# 使用r2_score评价模型在测试集和训练集上的性能，并输出评估结果\n",
    "#测试集\n",
    "print ('The r2 score of LinearRegression on test is', r2_score(y_day_test, lr_y_predict))\n",
    "#训练集\n",
    "print ('The r2 score of LinearRegression on train is', r2_score(y_day_train, lr_y_predict_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(291, 1)"
      ]
     },
     "execution_count": 249,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_day_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(291, 1)"
      ]
     },
     "execution_count": 250,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr_y_predict_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x218718c0eb8>"
      ]
     },
     "execution_count": 251,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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mSVKBDHBJkgpkgEuSVCADXJKkAhngkiQVyACXJKlABrgkSQUywCVJKpABLklSgQxwSZIK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\nwCVJKpABLklSgQxwSZIKZIBLklSg/w9EIDrj1C/LggAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x21871ef8438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(figsize=(7, 5))\n",
    "f.tight_layout()\n",
    "ax.hist(y_day_train - lr_y_predict_train, bins = 40, label = 'Residuals Linear', color = \"g\", normed = True)\n",
    "ax.set_title(\"Histogram of Residuals\")\n",
    "ax.legend(loc = 'best')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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GwDCtsmFOnjyJbt26YciQIXjhhRfQuHFjvZdEUUjrsE1mcIiIdOY42u4IZiwlFVi09SC0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\nOAAAIHP+ByENiHkLu4ohAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x21871c10f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#观察预测值与真值的散点图\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.scatter(y_day_train, lr_y_predict_train)\n",
    "#数据已经标准化，3倍残差即可\n",
    "plt.plot([-3, 3], [-3, 3], '--k')\n",
    "plt.axis('tight')\n",
    "plt.xlabel(\"True cnt\")\n",
    "plt.ylabel(\"Predicted cnt\")\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "对hour数据进行OLS缺省参数的线性回归"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  1.14107463e-01,   2.77555756e-17,   3.40254300e-02,\n",
       "          3.14080824e-01,  -1.89125958e-02,  -1.78442938e-03,\n",
       "         -6.15767606e-03,  -2.60722948e-02,   3.58264881e-01,\n",
       "         -2.08668934e-01,   3.20236460e-02]])"
      ]
     },
     "execution_count": 289,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#线性回归\n",
    "#class sklearn,linear_model.LinearRegression(fit_intercept=True, normalize=False, copy_X=True, n_jobs=1)\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "#使用没人配置初始化\n",
    "lr = LinearRegression()\n",
    "\n",
    "#训练模型参数\n",
    "model = lr.fit(X_hour_train, y_hour_train)\n",
    "\n",
    "#预测，下面计算score会自动调用predict\n",
    "lr_y_predict = model.predict(X_hour_test)\n",
    "lr_y_predict_train = model.predict(X_hour_train)\n",
    "\n",
    "#显示特征的回归系数\n",
    "lr.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The r2 score of LinearRegression on test is 0.368945234006\n",
      "The r2 score of LinearRegression on train is 0.388670892424\n"
     ]
    }
   ],
   "source": [
    "# 使用r2_score评价模型在测试集和训练集上的性能，并输出评估结果\n",
    "#测试集\n",
    "print ('The r2 score of LinearRegression on test is', r2_score(y_hour_test, lr_y_predict))\n",
    "#训练集\n",
    "print ('The r2 score of LinearRegression on train is', r2_score(y_hour_train, lr_y_predict_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x21872729ba8>"
      ]
     },
     "execution_count": 258,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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qgIEtSVIBDGxJkgpgYEuSVIBKN/+QShXXRqV2OdubzEnq3+xhS5JUAANbkqQCGNiSJBXA\nwJYkqQAGtiRJBTCwJUkqgIEtSVIBDGxJkgpgYEuSVAADW5KkAhjYkiQVwMCWJKkABrYkSQUwsCVJ\nKoCBLUlSAQxsSZIKYGBLklQAA1uSpAIY2JIkFcDAliSpAJUCOyKmRMTDEdEeEVf10OY/R8SDEbEy\nIr7Z3DIlSRrY9u+tQUQMAuYBbwY6gKURsSgzH2xoMxa4GnhDZv4+Ikb0VcGSJA1EvQY2MBFoz8xH\nASJiATAdeLChzWXAvMz8PUBmrmt2oSpXXButLkGSildlSHwk8HjDdkf9uUbHAsdGxH0R8bOImNLd\ngSJiVkQsi4hlnZ2du1exJEkDUJXA7q57lF229wfGAmcBFwBfjohDdvimzPmZ2ZaZbcOHD9/VWiVJ\nGrCqBHYHcGTD9ihgTTdt7sjMTZn5G+BhagEuSZKaoEpgLwXGRsSYiDgAmAEs6tLmu8DZABFxGLUh\n8kebWagkSQNZr4GdmZuBK4A7gVXA7Zm5MiLmRsS0erM7gfUR8SBwD/CRzFzfV0VLkjTQVJklTmYu\nBhZ3ee6ahscJfLj+JUmSmqxSYEv7ul356FnO7jrnUpL6nkuTSpJUAANbkqQCGNiSJBXAwJYkqQAG\ntiRJBTCwJUkqgIEtSVIBDGxJkgpgYEuSVAADW5KkAhjYkiQVwMCWJKkABrYkSQUwsCVJKoCBLUlS\nAQxsSZIKYGBLklQAA1uSpAIY2JIkFcDAliSpAAa2JEkFMLAlSSqAgS1JUgEMbEmSCmBgS5JUAANb\nkqQCGNiSJBXAwJYkqQD7t7oAqTRxbVRql7OzjyuRNJDYw5YkqQAGtiRJBTCwJUkqQKXAjogpEfFw\nRLRHxFXd7L8kIjojYkX96z3NL1WSpIGr10lnETEImAe8GegAlkbEosx8sEvT2zLzij6oUZKkAa9K\nD3si0J6Zj2bmRmABML1vy5IkSY2qBPZI4PGG7Y76c139RUTcHxELI+LI7g4UEbMiYllELOvs7NyN\nciVJGpiqBHZ3Hzrt+gHTfwJGZ+aJwD8DX+/uQJk5PzPbMrNt+PDhu1apJEkDWJWFUzqAxh7zKGBN\nY4PMXN+w+Q/AZ/a8NPV3VRcQkSTtuSo97KXA2IgYExEHADOARY0NIuLwhs1pwKrmlShJknrtYWfm\n5oi4ArgTGATclJkrI2IusCwzFwF/GRHTgM3A74BL+rBmSZIGnEpriWfmYmBxl+euaXh8NXB1c0uT\nJElbudKZJEkFMLAlSSqAgS1JUgEMbEmSCmBgS5JUAANbkqQCGNiSJBXAwJYkqQAGtiRJBTCwJUkq\ngIEtSVIBDGxJkgpgYEuSVAADW5KkAlS6vaakXRfXRqV2OTv7uBJJ+wJ72JIkFcDAliSpAAa2JEkF\nMLAlSSqAgS1JUgEMbEmSCmBgS5JUAANbkqQCGNiSJBXAwJYkqQAGtiRJBXAtce2g6hrYkqS9xx62\nJEkFMLAlSSqAgS1JUgEMbEmSCmBgS5JUAANbkqQCVArsiJgSEQ9HRHtEXLWTdu+IiIyItuaVKEmS\neg3siBgEzAPeCowDLoiIcd20Oxj4S+DnzS5SkqSBrkoPeyLQnpmPZuZGYAEwvZt2nwA+C7zQxPok\nSRLVAnsk8HjDdkf9uW0iYgJwZGZ+b2cHiohZEbEsIpZ1dnbucrGSJA1UVQK7u3Uqc9vOiP2AzwN/\n3duBMnN+ZrZlZtvw4cOrVylJ0gBXJbA7gCMbtkcBaxq2DwaOB5ZExGPAacAiJ55JktQ8VQJ7KTA2\nIsZExAHADGDR1p2Z+UxmHpaZozNzNPAzYFpmLuuTiiVJGoB6DezM3AxcAdwJrAJuz8yVETE3Iqb1\ndYGSJKni7TUzczGwuMtz1/TQ9qw9L0uSJDVypTNJkgpgYEuSVAADW5KkAhjYkiQVwMCWJKkABrYk\nSQUwsCVJKoCBLUlSAQxsSZIKYGBLklQAA1uSpAIY2JIkFcDAliSpAAa2JEkFMLAlSSqAgS1JUgEM\nbEmSCmBgS5JUAANbkqQCGNiSJBXAwJYkqQAGtiRJBTCwJUkqgIEtSVIBDGxJkgpgYEuSVID9W12A\nNNDFtVGpXc7OPq5EUn9mD1uSpAIY2JIkFcAh8QGi6rCrJKl/soctSVIBDGxJkgpgYEuSVIBKgR0R\nUyLi4Yhoj4irutn/XyPiVxGxIiJ+EhHjml+qJEkDV6+BHRGDgHnAW4FxwAXdBPI3M/OEzBwPfBb4\nXNMrlSRpAKvSw54ItGfmo5m5EVgATG9skJnPNmweBLjCgyRJTVTlY10jgccbtjuAU7s2ioj3Ax8G\nDgDe1N2BImIWMAvgqKOO2tVaJUkasKr0sLv7AO8OPejMnJeZxwAfBf62uwNl5vzMbMvMtuHDh+9a\npZIkDWBVArsDOLJhexSwZiftFwBv35OiJEnS9qoE9lJgbESMiYgDgBnAosYGETG2YXMq8EjzSpQk\nSb1ew87MzRFxBXAnMAi4KTNXRsRcYFlmLgKuiIhzgE3A74GL+7JoSZIGmkpriWfmYmBxl+euaXj8\nwSbXJUmSGrjSmSRJBTCwJUkqgLfXlAqxK7dIzdmuXSTta+xhS5JUAANbkqQCGNiSJBXAwJYkqQAG\ntiRJBTCwJUkqgIEtSVIBDGxJkgpgYEuSVABXOivcrqx+JUkqlz1sSZIKYGBLklQAA1uSpAIY2JIk\nFcDAliSpAAa2JEkFMLAlSSqAgS1JUgEMbEmSCmBgS5JUAANbkqQCGNiSJBXAwJYkqQAGtiRJBTCw\nJUkqgIEtSVIBDGxJkgpgYEuSVAADW5KkAhjYkiQVoFJgR8SUiHg4Itoj4qpu9n84Ih6MiPsj4u6I\neFXzS5UkaeDqNbAjYhAwD3grMA64ICLGdWn2r0BbZp4ILAQ+2+xCJUkayKr0sCcC7Zn5aGZuBBYA\n0xsbZOY9mfl8ffNnwKjmlilJ0sBWJbBHAo83bHfUn+vJTOAHe1KUJEna3v4V2kQ3z2W3DSMuBNqA\nM3vYPwuYBXDUUUdVLFGSJFUJ7A7gyIbtUcCaro0i4hzg48CZmfmn7g6UmfOB+QBtbW3dhr6kPRfX\ndvd39o5ytv8bSqWoMiS+FBgbEWMi4gBgBrCosUFETAC+BEzLzHXNL1OSpIGt18DOzM3AFcCdwCrg\n9sxcGRFzI2Javdl1wFDg2xGxIiIW9XA4SZK0G6oMiZOZi4HFXZ67puHxOU2uS5IkNXClM0mSClCp\nhy1pYHMSm9R69rAlSSqAgS1JUgEMbEmSCmBgS5JUAANbkqQCGNiSJBXAwJYkqQAGtiRJBTCwJUkq\ngCudSQNY1RXMJLWePWxJkgpgYEuSVAADW5KkAhjYkiQVwMCWJKkAzhKX1BLeY1vaNfawJUkqgIEt\nSVIBHBLvp1zQQpLUyB62JEkFMLAlSSqAgS1JUgEMbEmSCuCkM0lN42RJqe/Yw5YkqQAGtiRJBTCw\nJUkqgIEtSVIBDGxJkgrgLHFJ/Zp39ZJq7GFLklSASoEdEVMi4uGIaI+Iq7rZf0ZE/DIiNkfEO5pf\npiRJA1uvgR0Rg4B5wFuBccAFETGuS7P/B1wCfLPZBUqSpGrXsCcC7Zn5KEBELACmAw9ubZCZj9X3\nvdgHNUqSNOBVGRIfCTzesN1Rf06SJO0lVQK7uymauzUdMyJmRcSyiFjW2dm5O4eQJGlAqjIk3gEc\n2bA9ClizOy+WmfOB+QBtbW1+BkPSXrcrNyjxo2LqT6r0sJcCYyNiTEQcAMwAFvVtWZIkqVGvgZ2Z\nm4ErgDuBVcDtmbkyIuZGxDSAiDglIjqAdwJfioiVfVm0JEkDTaWVzjJzMbC4y3PXNDxeSm2oXJJa\nwntxa1/n0qR7mb9UJEm7w6VJJUkqgIEtSVIBDGxJkgpgYEuSVAAnnUlSD7wXt/oTe9iSJBXAwJYk\nqQAGtiRJBTCwJUkqgJPOJGkv8U5h2hMGtiTtIZcc1t7gkLgkSQWwhy1JA4CfKS+fPWxJkgpgYEuS\nVACHxJvACSeSpL5mD1uSpAIY2JIkFcDAliSpAF7DlqR+qFVzY1yNrf+yhy1JUgEMbEmSCuCQ+E74\ncS1J6lmzf0c6xL5z9rAlSSqAgS1JUgEMbEmSCmBgS5JUACedSZL6BW8BunP2sCVJKoCBLUlSAQxs\nSZIKMCCvYbsgiiSVqy9+h5dwXXxABrYkSY1KmPBWKbAjYgrwBWAQ8OXM/HSX/QcCNwOvB9YD52fm\nY80ttUKd9pwlSfuoXq9hR8QgYB7wVmAccEFEjOvSbCbw+8x8NfB54DPNLlSSpIGsyqSziUB7Zj6a\nmRuBBcD0Lm2mA1+vP14ITI4Iu7uSJDVJlSHxkcDjDdsdwKk9tcnMzRHxDHAo8FRjo4iYBcyqb26I\niPVd22iPHYbntNk8p33D89p8ntPm2+6cxpw+6Yu+qkqjKoHdXXVdr7pXaUNmzgfmb/umiGWZ2Vah\nBlXkOW0+z2nf8Lw2n+e0+frTOa0yJN4BHNmwPQpY01ObiNgfGAb8rhkFSpKkaoG9FBgbEWMi4gBg\nBrCoS5tFwMX1x+8AfpSZ/f9DbZIkFaLXIfH6NekrgDupfazrpsxcGRFzgWWZuQj4CvCNiGin1rOe\nUfH15/feRLvIc9p8ntO+4XltPs9p8/Wbcxp2hCVJ6v9cS1ySpAIY2JIkFaDlgR0R10XEQxFxf0R8\nJyIOaXVNpYuId0bEyoh4MSL6xccRShURUyLi4Yhoj4irWl1P6SLipohYFxEPtLqWfUVEHBkR90TE\nqvr/9x9sdU2li4ghEfGLiPi3+jm9ttU1QT8IbOCHwPGZeSLwa+DqFtezL3gA+E/Ava0upGQVl+XV\nrvkaMKXVRexjNgN/nZmvA04D3u9/p3vsT8CbMvMkYDwwJSJOa3FNrQ/szLwrMzfXN39G7XPe2gOZ\nuSozH251HfuAKsvyahdk5r24RkNTZebazPxl/fEfgFXUVp/UbsqaDfXNwfWvls/Qbnlgd3Ep8INW\nFyHVdbcsr78I1W9FxGhgAvDz1lZSvogYFBErgHXADzOz5ed0r9wPOyL+GXhlN7s+npl31Nt8nNrQ\nzq17o6bSVTmn2mOVltyV+oOIGAr8b+CvMvPZVtdTuszcAoyvz6v6TkQcn5ktnXuxVwI7M8/Z2f6I\nuBg4D5jsCmnV9HZO1RRVluWVWi4iBlML61sz8x9bXc++JDOfjogl1OZetDSwWz4kHhFTgI8C0zLz\n+VbXIzWosiyv1FL1Wxl/BViVmZ9rdT37gogYvvUTSxHxEuAc4KHWVtUPAhu4ETgY+GFErIiI/9Xq\ngkoXEX8eER3AJOD7EXFnq2sqUX0y5NZleVcBt2fmytZWVbaI+BbwU+A1EdERETNbXdM+4A3ARcCb\n6r9DV0TEf2x1UYU7HLgnIu6n9of7DzPzey2uyaVJJUkqQX/oYUuSpF4Y2JIkFcDAliSpAAa2JEkF\nMLAlSSqAgS1JUgEMbEmSCvD/AQSoPk1KtAQsAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x218718e0048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f_h, ax_h = plt.subplots(figsize=(7, 5))\n",
    "f_h.tight_layout()\n",
    "ax_h.hist(y_hour_train - lr_y_predict_train, bins = 40, label = 'Residuals Linear', color = \"g\", normed = True)\n",
    "ax_h.set_title(\"Histogram of Residuals\")\n",
    "ax_h.legend(loc = 'best')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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CV59+GDznZyg96fSsP2axf6tDUmLus1tQIoCgjV9s4tDNWItX78jq81oINU9W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\nAADSDgUHAACkHQoOAABIOxQcAACQdv4PsPcSzMWfxGEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x21872e2cc18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#观察预测值与真值的散点图\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.scatter(y_hour_train, lr_y_predict_train)\n",
    "#数据已经标准化，3倍残差即可\n",
    "plt.plot([-3, 3], [-3, 3], '--k')\n",
    "plt.axis('tight')\n",
    "plt.xlabel(\"True cnt\")\n",
    "plt.ylabel(\"Predicted cnt\")\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2.2 岭回归模型"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "对day数据进行岭回归"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import RidgeCV\n",
    "\n",
    "day_alphas = [0.01, 0.1, 1, 10, 20, 40, 80, 100]\n",
    "day_reg = RidgeCV(alphas=day_alphas, store_cv_values=True)\n",
    "day_reg.fit(X_day_train, y_day_train)\n",
    "#预测\n",
    "day_reg_y_predict = day_reg.predict(X_day_test)\n",
    "day_reg_y_predict_train = day_reg.predict(X_day_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "最优参数alpha为1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 263,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "day_reg.alpha_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Vm7lvzfN0HOxk6vhRXP7GWSyZP43XThvv2RDLyMmkBw+sfYEdHYdesb67/6OF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\niZmZ9ZuTiZmZ9dv/B0GSVcLt2kIUAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x21870cb5860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "alpha is:  1.0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[ 0.31795565,  0.        , -0.04059797, -0.0578475 ,  0.04043642,\n",
       "         0.00967946, -0.21215618,  0.6437316 , -0.03605973, -0.11116116]])"
      ]
     },
     "execution_count": 273,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "day_mse_mean = np.mean(day_reg.cv_values_, axis = 0)\n",
    "plt.plot(np.log10(day_alphas), day_mse_mean.reshape(len(day_alphas), 1))\n",
    "plt.plot(np.log10(day_reg.alpha_)*np.ones(3), [0.28, 0.29, 0.30])\n",
    "plt.xlabel('log(alpha)')\n",
    "plt.ylabel('day_mse')\n",
    "plt.show()\n",
    "\n",
    "print('alpha is: ', day_reg.alpha_)\n",
    "day_reg.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The r2 score of RidgeCV on test is 0.75443466581\n",
      "The r2 score of RidgeCV on train is 0.752091241978\n"
     ]
    }
   ],
   "source": [
    "# 评估，使用r2_score评价模型在测试集和训练集上的性能\n",
    "print ('The r2 score of RidgeCV on test is', r2_score(y_day_test, day_reg_y_predict))\n",
    "print ('The r2 score of RidgeCV on train is', r2_score(y_day_train, day_reg_y_predict_train))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "对hour做岭回归"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "hour_alphas = [0.01, 0.1, 1, 10, 20, 40, 80, 100]\n",
    "hour_reg = RidgeCV(alphas=hour_alphas, store_cv_values=True)\n",
    "hour_reg.fit(X_hour_train, y_hour_train)\n",
    "#预测\n",
    "hour_reg_y_predict = hour_reg.predict(X_hour_test)\n",
    "hour_reg_y_predict_train = hour_reg.predict(X_hour_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "40.0"
      ]
     },
     "execution_count": 269,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#最优参数\n",
    "hour_reg.alpha_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x21872750748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "alpha is:  40.0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[ 0.11312358,  0.        ,  0.03494405,  0.31273252, -0.01877674,\n",
       "        -0.00180876, -0.00596801, -0.02658667,  0.35642836, -0.2075793 ,\n",
       "         0.03221407]])"
      ]
     },
     "execution_count": 287,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hour_mse_mean = np.mean(hour_reg.cv_values_, axis = 0)\n",
    "plt.plot(np.log10(hour_alphas), hour_mse_mean.reshape(len(hour_alphas), 1))\n",
    "plt.plot(np.log10(hour_reg.alpha_)*np.ones(3), [0.28, 0.29, 0.30])\n",
    "plt.xlabel('log(alpha)')\n",
    "plt.ylabel('hour_mse')\n",
    "plt.show()\n",
    "\n",
    "print('alpha is: ', hour_reg.alpha_)\n",
    "hour_reg.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The r2 score of RidgeCV on test is 0.368966151357\n",
      "The r2 score of RidgeCV on train is 0.388662995019\n"
     ]
    }
   ],
   "source": [
    "# 评估，使用r2_score评价模型在测试集和训练集上的性能\n",
    "print ('The r2 score of RidgeCV on test is', r2_score(y_hour_test, hour_reg_y_predict))\n",
    "print ('The r2 score of RidgeCV on train is', r2_score(y_hour_train, hour_reg_y_predict_train))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2.3 Lasso模型"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 对day数据做lasso"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\envs\\python3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:1094: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LassoCV(alphas=[1e-06, 1e-05, 0.0001, 0.001, 0.01], copy_X=True, cv=None,\n",
       "    eps=0.001, fit_intercept=True, max_iter=1000, n_alphas=100, n_jobs=1,\n",
       "    normalize=False, positive=False, precompute='auto', random_state=None,\n",
       "    selection='cyclic', tol=0.0001, verbose=False)"
      ]
     },
     "execution_count": 276,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "###Lasso/ L1正则\n",
    "# class sklearn.linear_model.LassoCV(eps=0.001, n_alphas=100, alphas=None, fit_intercept=True, \n",
    "#                                    normalize=False, precompute=’auto’, max_iter=1000, \n",
    "#                                    tol=0.0001, copy_X=True, cv=None, verbose=False, n_jobs=1,\n",
    "#                                    positive=False, random_state=None, selection=’cyclic’)\n",
    "from sklearn.linear_model import  LassoCV\n",
    "\n",
    "day_alphas = [0.000001, 0.00001, 0.0001, 0.001, 0.01]\n",
    "\n",
    "day_lasso = LassoCV(alphas=day_alphas)\n",
    "\n",
    "day_lasso.fit(X_day_train, y_day_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.01"
      ]
     },
     "execution_count": 277,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#预测\n",
    "lasso_y_day_predict = day_lasso.predict(X_day_test)\n",
    "lasso_y_predict_day_train = day_lasso.predict(X_day_train)\n",
    "day_lasso.alpha_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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eXrGNvYdaoi4nqyVzmesf3X2/mX0YuByYC9yXnrJERLLDrPoaDre08eTyrVGXktWSCZOO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\nIin7/4L4Bw92BSA1AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x218725d6a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "alpha is:  [1e-06, 1e-05, 0.0001, 0.001, 0.01]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([ 0.27511278,  0.        , -0.        , -0.05330667,  0.02884609,\n",
       "        0.        , -0.20707574,  0.64003161, -0.02409642, -0.09845223])"
      ]
     },
     "execution_count": 278,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "day_mses = np.mean(day_lasso.mse_path_, axis = 1)\n",
    "plt.plot(np.log10(day_lasso.alphas_), day_mses)\n",
    "#plt.plot(np.log10(lasso.alphas_)*np.ones(3), [0.3, 0.4, 1.0])\n",
    "plt.xlabel('log(day_alpha)')\n",
    "plt.ylabel('day_mse')\n",
    "plt.show()\n",
    "\n",
    "print (\"alpha is: \", day_lasso.alphas)\n",
    "day_lasso.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The r2 score of LinearRegression on test is 0.753127428811\n",
      "The r2 score of LinearRegression on train is 0.750816023539\n"
     ]
    }
   ],
   "source": [
    "# 使用r2_score评价模型在测试集和训练集上的性能，并输出评估结果\n",
    "#测试集\n",
    "print ('The r2 score of LinearRegression on test is', r2_score(y_day_test, lasso_y_day_predict))\n",
    "#训练集\n",
    "print ('The r2 score of LinearRegression on train is', r2_score(y_day_train, lasso_y_predict_day_train))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 对hour数据做lasso"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\envs\\python3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:1094: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LassoCV(alphas=[1e-06, 1e-05, 0.0001, 0.001, 0.01], copy_X=True, cv=None,\n",
       "    eps=0.001, fit_intercept=True, max_iter=1000, n_alphas=100, n_jobs=1,\n",
       "    normalize=False, positive=False, precompute='auto', random_state=None,\n",
       "    selection='cyclic', tol=0.0001, verbose=False)"
      ]
     },
     "execution_count": 298,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hour_alphas = [0.000001, 0.00001, 0.0001, 0.001, 0.01]\n",
    "\n",
    "hour_lasso = LassoCV(alphas=hour_alphas)\n",
    "\n",
    "hour_lasso.fit(X_hour_train, y_hour_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.001"
      ]
     },
     "execution_count": 300,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#预测\n",
    "lasso_y_hour_predict = hour_lasso.predict(X_hour_test)\n",
    "lasso_y_predict_hour_train = hour_lasso.predict(X_hour_train)\n",
    "hour_lasso.alpha_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ZTMhM5deDrPHgrV0NNJxoZfn88W5HiVpWfIwxYRMXJ9xbnE9lzTG2eI+6HSdi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\nyUQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x218724facf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "alpha is:  [1e-06, 1e-05, 0.0001, 0.001, 0.01]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([ 0.11379617,  0.        ,  0.03309345,  0.31342673, -0.0174689 ,\n",
       "       -0.00065574, -0.00481702, -0.02549511,  0.35772829, -0.20806061,\n",
       "        0.03095815])"
      ]
     },
     "execution_count": 304,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hour_mses = np.mean(hour_lasso.mse_path_, axis = 1)\n",
    "plt.plot(np.log10(hour_lasso.alphas_), hour_mses)\n",
    "#plt.plot(np.log10(hour_lasso.alphas_)*np.ones(3), [0.3, 0.4, 1.0])\n",
    "plt.xlabel('log(hour_alpha)')\n",
    "plt.ylabel('day_mse')\n",
    "plt.show()\n",
    "\n",
    "print (\"alpha is: \", hour_lasso.alphas)\n",
    "hour_lasso.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The r2 score of LinearRegression on test is 0.368997281852\n",
      "The r2 score of LinearRegression on train is 0.388662303688\n"
     ]
    }
   ],
   "source": [
    "# 使用r2_score评价模型在测试集和训练集上的性能，并输出评估结果\n",
    "#测试集\n",
    "print ('The r2 score of LinearRegression on test is', r2_score(y_hour_test, lasso_y_hour_predict))\n",
    "#训练集\n",
    "print ('The r2 score of LinearRegression on train is', r2_score(y_hour_train, lasso_y_predict_hour_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
